1 Introduction

Objectives: The goal of this kernel is to analyze the effect and flavor of 3 different types of Cannabis.

EDA includes datatable, skim, 3D plotly, DrillDown and NLP.

If you have any question, please leave a comment and if you like the kernel, please give me an upvote~ Thanks!

2 Basic Set up

2.1 Load Packages

library(tidyverse)
library(skimr)
library(highcharter)
library(tm)
library(plotly)
library(viridis)
library(wordcloud)
library(plotrix)
library(DescTools)
library(DT)
library(ggraph)
library(igraph)
library(ggthemes)
library(visNetwork)
library(tidytext)

2.2 Load Dataset

set.seed(1)
weed <- read_csv("../data/cannabis.csv")

3 Glimpses

3.1 skim

# weed %>% skim() %>% kable()
weed %>% skim()

Data summary
Name	Piped data
Number of rows	2351
Number of columns	6
_______________________
Column type frequency:
character	5
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	n_unique
Strain	0	1.00	2	30	2350
Type	0	1.00	6	6	3
Effects	0	1.00	4	46	1655
Flavor	46	0.98	3	30	1293
Description	33	0.99	4	1120	2312

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
Rating	0	1	4.31	0.84	0	4.2	4.4	4.7	5	▁▁▁▁▇

3.2 datatable

weed %>% 

  select(-Description) %>%
  datatable(filter = 'top', options = list(
  pageLength = 30, autoWidth = TRUE

))

3.3 glimpse

weed %>% glimpse()

## Rows: 2,351
## Columns: 6
## $ Strain      <chr> "100-Og", "98-White-Widow", "1024", "13-Dawgs", "24K-Gold"~
## $ Type        <chr> "hybrid", "hybrid", "sativa", "hybrid", "hybrid", "indica"~
## $ Rating      <dbl> 4.0, 4.7, 4.4, 4.2, 4.6, 0.0, 4.4, 4.2, 4.6, 4.4, 4.7, 4.5~
## $ Effects     <chr> "Creative,Energetic,Tingly,Euphoric,Relaxed", "Relaxed,Aro~
## $ Flavor      <chr> "Earthy,Sweet,Citrus", "Flowery,Violet,Diesel", "Spicy/Her~
## $ Description <chr> "$100 OG is a 50/50 hybrid strain that packs a strong punc~

4 Cannabis Ranking Distribution

weed$Rating %>% PlotFdist("Cannabis Ranking Distribution")

Ranking by Types

type <- weed$Type %>% unique()

for (i in 1:length(type)){
weed[weed$Type == type[i],]$Rating %>% 
  PlotFdist(paste("Cannabis Ranking Distribution - ", toupper(type[i])))
}

5 Cannabis Ranking Boxplot by Type

hcboxplot(weed$Rating, weed$Type, color = 'firebrick') %>% 
  hc_add_theme(hc_theme_economist()) %>% 
  hc_chart(type = 'bar') %>%
  hc_title(text = "Cannbis Type Ranking Boxplot")

6 TreeMaps

6.1 TreeMap by Type

by_type <- weed %>% 
  count(Type)
hchart(by_type, type = 'treemap', hcaes(x = 'Type', value = 'n', color = 'n'))

rm(by_type)

6.2 TreeMap by Effects

weed_effects <- weed %>% 
  mutate(Effects = str_split(Effects,',')) %>% 
  unnest(Effects)


weed_effects %>% 
  count(Effects) %>% 
  hchart(type = 'treemap', hcaes(x = 'Effects', value = 'n', color = 'n'))

6.3 TreeMap by Flavors

weed_flavor <- weed %>% 
  filter(Flavor != 'none') %>% 
  mutate(Flavor = str_split(Flavor, ',')) %>% 
  unnest(Flavor)


weed_flavor %>% 
  count(Flavor) %>% 
  hchart(type = 'treemap', hcaes(x = 'Flavor', value = 'n', color = 'n'))

7 Rating by Effects

filtered_weed_effects <- weed_effects %>% filter(Effects != "None")
hcboxplot(filtered_weed_effects $Rating, filtered_weed_effects $Effects, color = 'firebrick') %>% 
  hc_add_theme(hc_theme_economist()) %>% 
  hc_chart(type = 'bar') %>%
  hc_title(text = "Cannbis Effects Ranking Boxplot")

8 Rating by Flavors

filtered_weed_flavor <- weed_flavor %>% filter(Flavor != "None")
hcboxplot(filtered_weed_flavor$Rating, filtered_weed_flavor$Flavor, color = 'firebrick') %>% 
  hc_add_theme(hc_theme_economist()) %>% 
  hc_chart(type = 'bar') %>%
  hc_title(text = "Cannbis Flavor Ranking Boxplot")

9 Interactive Drill Down Graph for 3 Types by Effects

Click the part to Drill Down!

df1 <- weed %>% 
  group_by(name = Type, drilldown = Type) %>% 
  summarise(y = n()) %>% 
  arrange(desc(y))

df2 <- weed_effects %>% 
  group_by(Type, Effects) %>% 
  mutate(y = n()
         # , colorByPoint =  1
         ) %>% 
  arrange(desc(y)) %>%
  group_by(name = Type, id = Type
           # , colorByPoint
           ) %>% 
  do(data = list_parse(
    mutate(.,name = Effects, drilldown = tolower(paste(Type, Effects,sep=": "))) %>% 
      group_by(name, drilldown) %>% 
      summarise(y=n()) %>% 
      select(name, y, drilldown) %>%
      arrange(desc(y)))) 

a <- highchart() %>% 
  hc_chart(type = 'pie') %>% 
  hc_xAxis(type = "category") %>% 
  hc_add_series(name = 'number of cannabis', data = df1, colorByPoint = 1) %>% 
  hc_drilldown(
    allowPointDrilldown = TRUE,
    series =list_parse(df2)
  ) %>%
  hc_legend(enabled = F) %>% 
  hc_title(text = "Type of Cannbis vs Effects Pie Chart") %>% 
  hc_add_theme(hc_theme_darkunica())

b <- highchart() %>% 
  hc_chart(type = 'bar') %>% 
  hc_xAxis(type = "category") %>% 
  hc_add_series(name = 'number of cannabis', data = df1, colorByPoint = 1) %>% 
  hc_drilldown(
    allowPointDrilldown = TRUE,
    series =list_parse(df2)
  ) %>%
  hc_legend(enabled = F) %>% 
  hc_title(text = "Type of Cannbis vs Effects Bar Chart") %>% 
  hc_add_theme(hc_theme_darkunica())
rm(df1, df2)



lst <- list(
  a,
  b
)



hw_grid(lst, rowheight = 400)

rm(a, b, lst)

10 Interactive Drill Down Graph for 3 Types by Flavors

Click the part to Drill Down!

df1 <- weed %>% 
  group_by(name = Type, drilldown = Type) %>% 
  summarise(y = n()) %>% 
  arrange(desc(y))

df2 <- weed_flavor %>% 
  group_by(Type, Flavor) %>% 
  mutate(y = n(), colorByPoint = 1) %>% 
  arrange(desc(y)) %>%
  group_by(name = Type, id = Type
           # , colorByPoint
           ) %>% 
  do(data = list_parse(
    mutate(., name = Flavor, drilldown = tolower(paste(Type, Flavor,sep=": "))) %>% 
      group_by(name, drilldown) %>% 
      summarise(y=n()) %>% 
      select(name, y, drilldown) %>%
      arrange(desc(y)))) 

a <- highchart() %>% 
  hc_chart(type = 'pie') %>% 
  hc_xAxis(type = "category") %>% 
  hc_add_series(name = 'number of cannabis', data = df1
                # , colorByPoint = 1
                ) %>% 
  hc_drilldown(
    allowPointDrilldown = TRUE,
    series =list_parse(df2)
  ) %>%
  hc_legend(enabled = F) %>% 
  hc_title(text = "Type of Cannbis vs Flavor Pie Chart") %>% 
  hc_add_theme(hc_theme_darkunica())


b <- highchart() %>% 
  hc_chart(type = 'bar') %>% 
  hc_xAxis(type = "category") %>% 
  hc_add_series(name = 'number of cannabis', data = df1, colorByPoint = 1) %>% 
  hc_drilldown(
    allowPointDrilldown = TRUE,
    series =list_parse(df2)
  ) %>%
  hc_legend(enabled = F) %>% 
  hc_title(text = "Type of Cannbis vs Flavor Bar Chart") %>% 
  hc_add_theme(hc_theme_darkunica())
rm(df1, df2)



lst <- list(
  a,
  b
)



hw_grid(lst, rowheight = 400)

rm(a, b, lst)

11 NLP Setup

11.1 clean corpus

# clean corpus
cleanCorpus <- function(corpus){
  
  corpus.tmp <- tm_map(corpus, removePunctuation)
  corpus.tmp <- tm_map(corpus.tmp, stripWhitespace)
  corpus.tmp <- tm_map(corpus.tmp, content_transformer(tolower))
  v_stopwords <- c(stopwords("en"), c("thats","weve","hes","theres","ive","im","strain",
                                      "will","can","cant","dont","youve","us",
                                      "youre","youll","theyre","whats","didnt"))
  corpus.tmp <- tm_map(corpus.tmp, removeWords, v_stopwords)
  corpus.tmp <- tm_map(corpus.tmp, removeNumbers)
  return(corpus.tmp)
}

11.2 frequent terms

# frequent terms 
frequentTerms <- function(text){
  
  s.cor <- Corpus(VectorSource(text))
  s.cor.cl <- cleanCorpus(s.cor)
  s.tdm <- TermDocumentMatrix(s.cor.cl)
  s.tdm <- removeSparseTerms(s.tdm, 0.999)
  m <- as.matrix(s.tdm)
  word_freqs <- sort(rowSums(m), decreasing=TRUE)
  dm <- data.frame(word=names(word_freqs), freq=word_freqs)
  return(dm)
  
}

11.3 clean by each type

# clean by each Type
clean_top_char <- function(dataset){
  all_dialogue <- list()
  namelist <- list()
  
  for (i in 1:3){
    top <- dataset %>% count(Type) %>% arrange(desc(n)) %>% head(20)
    name <- top$Type[i]
    Description <- paste(dataset$Description[dataset$Type == name], collapse = " ")
    all_dialogue <- c(all_dialogue, Description)
    namelist <- c(namelist, name)
    
  }
  
  
  
  all_clean <- all_dialogue %>% 
    VectorSource() %>% 
    Corpus() %>% 
    cleanCorpus() %>% 
    TermDocumentMatrix() %>%
    as.matrix()
  
  colnames(all_clean) <- namelist
  
  assign("all_clean",all_clean,.GlobalEnv)
  all_clean %>% head()
}

weed %>% clean_top_char()

##              Docs
## Terms         hybrid indica sativa
##   abandon          1      0      0
##   abate            8      5      5
##   abates           1      1      0
##   abating          3      1      0
##   abbreviated      1      0      0
##   abduct           1      0      0

12 Top 30 Words in Description

weed$Description %>% 
  frequentTerms() %>% 
  # dim()
  head(30) %>% 
  mutate(word = factor(word))%>% 
  plot_ly(x = ~reorder(word,-freq), y = ~freq, colors = viridis(10)) %>%
  add_bars(color = ~word) %>%
  layout(title = "Top 30 Words", 
         yaxis = list(title = " "), 
         xaxis = list(title = ""), 
         margin = list(l = 100))

13 Top 30 Words by Type

df <- all_clean %>% 
  as.data.frame() %>% 
  rownames_to_column() %>% 
  gather(type, value, -1)

df1 <- df %>% 
  group_by(name = type, drilldown = type) %>% 
  summarise(y = sum(value)) %>% 
  arrange(desc(y))


df2 <- df %>% 
  group_by(type, rowname) %>% 
  summarise(total = sum(value)) %>% 
  arrange(desc(total)) %>% 
  group_by(name = type, id = type) %>% 
  do(data = list_parse(
    mutate(., name = rowname, drilldown = tolower(paste(type, rowname, sep=": "))) %>% 
      group_by(name, drilldown) %>% 
      summarise(y = sum(total)) %>% 
      select(name, y, drilldown) %>%
      arrange(desc(y))) %>% 
      head(30))

  

highchart() %>% 
  hc_chart(type = 'column') %>% 
  hc_xAxis(type = "category") %>% 
  hc_add_series(name = 'number of words in comments', data = df1, colorByPoint = 1) %>% 
  hc_drilldown(
    allowPointDrilldown = TRUE,
    series =list_parse(df2)
  ) %>%
  hc_legend(enabled = F) %>% 
  hc_title(text = "Top 30 Words by Type of Cannbis") %>% 
  hc_add_theme(hc_theme_darkunica())

rm(df2)

14 Sentiment Analysis by Type

df2 <- df %>%
  inner_join(get_sentiments("loughran"), by = c('rowname' = 'word')) %>% 
  group_by(type,sentiment) %>% 
  summarise(total = sum(value)) %>% 
  arrange(desc(total)) %>% 
  group_by(name = type, id = type) %>% 
  do(data = list_parse(
    mutate(., name = sentiment, drilldown = tolower(paste(type, sentiment, sep = ": "))) %>% 
      group_by(name, drilldown) %>% 
      summarise(y = sum(total)) %>% 
      select(name, y , drilldown) %>% 
      arrange(desc(y))
  ))



highchart() %>% 
  hc_chart(type = 'column') %>% 
  hc_xAxis(type = "category") %>% 
  hc_add_series(name = 'number of words in comments', data = df1, colorByPoint = 1) %>% 
  hc_drilldown(
    allowPointDrilldown = TRUE,
    series =list_parse(df2)
  ) %>%
  hc_legend(enabled = F) %>% 
  hc_title(text = "Sentiment Analysis by Type of Cannbis") %>% 
  hc_add_theme(hc_theme_darkunica())

rm(df2)

15 WordCloud

15.1 Commonality Cloud

Sativa vs Indica

commonality.cloud(all_clean[,c("sativa","indica")], colors = "steelblue1", at.least = 2, max.words = 100)

15.2 Comparison Cloud

Sativa vs Indica

comparison.cloud(all_clean[,c("sativa","indica")], colors = c("#F8766D", "#00BFC4"), max.words=50)

16 Pramid Plot

Sativa vs Indica

common_words <- all_clean %>%
  as.data.frame() %>% 
  rownames_to_column() %>% 
  filter(sativa>0, indica>0) %>% 
  # select(sativa, indica)
  mutate(difference = abs(sativa - indica)) %>% 
  arrange(desc(difference)) 

common_words_25 <- common_words%>%
  head(25)

pyramid.plot(common_words_25$sativa, common_words_25$indica,
             labels = common_words_25$rowname, gap = 200,
             top.labels = c("sativa", "Words", "indica"),
             main = "Words in Common", laxlab = NULL, 
             raxlab = NULL, unit = NULL)

## 517 517

## [1] 5.1 4.1 4.1 2.1

# rm(common_words, common_words_25)

17 3D Plotly - Effects by type

effects <- weed_effects$Effects %>% unique() %>% tolower()
rm(weed_effects)


effectByType <- all_clean %>%
  as.data.frame() %>% 
  rownames_to_column('word') %>% 
  filter(word %in% effects) %>% 
  mutate(word=factor(word))
  

effectByType %>% 
  plot_ly(x=~hybrid,y=~sativa,z= ~indica, hoverinfo = 'text', colors = viridis(15),
          text = ~paste('Effects:', word
                        ,'<br>hybrid:', hybrid,
                        '<br>sativa:', sativa,
                        '<br>indica:', indica
                        )) %>% 
  add_markers(opacity = 0.8) %>%
  layout(title = "Effects by Different Cannabis",
         annotations=list(yref='paper',xref="paper",y=1.05,x=1.1, text="Effects",showarrow=F),
         scene = list(xaxis = list(title = 'hybrid'),
                      yaxis = list(title = 'sativa'),
                      zaxis = list(title = 'indica')))

18 Hybrid Networks

18.1 Hybrid Network 1

effectByType %>%
  mutate(type = 'hybrid') %>%
  select(word, type, hybrid) %>% 
  graph_from_data_frame() %>%
  ggraph(layout = "fr") +
  geom_edge_link(aes(edge_alpha = hybrid), show.legend = FALSE) +
  geom_node_point(color = "firebrick", size = 20, alpha = .5) +
  geom_node_text(aes(label = name), col = "white") +
  theme_solarized(light = F)

18.2 Hybrid Network 2

network3_edg <- effectByType %>% 
  mutate(type = 'hybrid') %>%
  select(word, type, hybrid) %>% 
  filter(hybrid>0) %>% 
  rename(from = type, to = word, weight = hybrid, width = hybrid)

network3_node <- effectByType %>% 
  mutate(type = 'hybrid') %>%
  filter(hybrid>0) %>% 
  select(word, hybrid) %>% 
  rename(id = word, size = hybrid)
network3_node$label <- network3_node$id # Node label

network3_node <- bind_rows(network3_node,data.frame(id = 'hybrid',  size = 24,  label = 'hybrid'))

visNetwork(network3_node, network3_edg, height = "500px", width = "100%") %>% 
  # visIgraphLayout(layout = "layout_with_lgl") %>% 
  visEdges(shadow = TRUE,
           color = list(color = "gray", highlight = "orange"))

18.3 Hybrid Network 3

network3_edg <- effectByType %>% 
  mutate(type = 'hybrid') %>%
  select(word, type, hybrid) %>% 
  filter(hybrid>0) %>% 
  rename(from = type, to = word, weight = hybrid, width = hybrid)

network3_node <- effectByType %>% 
  mutate(type = 'hybrid') %>%
  filter(hybrid>0) %>% 
  select(word, hybrid) %>% 
  rename(id = word, size = hybrid)
network3_node$label <- network3_node$id # Node label

network3_node <- bind_rows(network3_node,data.frame(id = 'hybrid',  size = 24,  label = 'hybrid'))

visNetwork(network3_node, network3_edg, height = "500px", width = "100%") %>% 
  visIgraphLayout(layout = "layout_with_lgl") %>% 
  visEdges(shadow = TRUE,
           color = list(color = "gray", highlight = "orange"))

18.4 Hybrid Network 4

network3_edg <- effectByType %>% 
  mutate(type = 'hybrid') %>%
  select(word, type, hybrid) %>% 
  filter(hybrid>0) %>% 
  rename(from = type, to = word)

network3_node <- effectByType %>% 
  mutate(type = 'hybrid') %>%
  filter(hybrid>0) %>% 
  select(word) %>% 
  rename(id = word)
network3_node$label <- network3_node$id # Node label

network3_node <- bind_rows(network3_node,data.frame(id = 'hybrid', label = 'hybrid'))

visNetwork(network3_node, network3_edg, height = "500px", width = "100%") %>% 
  # visIgraphLayout(layout = "layout_with_lgl") %>% 
  visEdges(shadow = TRUE,
           color = list(color = "gray", highlight = "orange"))

19 Conclusion

Hope you enjoyed the cannabis analysis

Cannabis Interactive Analysis

Guiliana Mustiga

2023-04-17

1 Introduction

2 Basic Set up

2.1 Load Packages

2.2 Load Dataset

3 Glimpses

3.1 skim

3.2 datatable

3.3 glimpse

4 Cannabis Ranking Distribution

5 Cannabis Ranking Boxplot by Type

6 TreeMaps

6.1 TreeMap by Type

6.2 TreeMap by Effects

6.3 TreeMap by Flavors

7 Rating by Effects

8 Rating by Flavors

9 Interactive Drill Down Graph for 3 Types by Effects

10 Interactive Drill Down Graph for 3 Types by Flavors

11 NLP Setup

11.1 clean corpus

11.2 frequent terms

11.3 clean by each type

12 Top 30 Words in Description

13 Top 30 Words by Type

14 Sentiment Analysis by Type

15 WordCloud

15.1 Commonality Cloud

15.2 Comparison Cloud

16 Pramid Plot

17 3D Plotly - Effects by type

18 Hybrid Networks

18.1 Hybrid Network 1

18.2 Hybrid Network 2

18.3 Hybrid Network 3

18.4 Hybrid Network 4

19 Conclusion